Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3677
Missing cells6710
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory578.1 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%) Imbalance
facing has 1045 (28.4%) missing values Missing
super_built_up_area has 1802 (49.0%) missing values Missing
built_up_area has 1987 (54.0%) missing values Missing
carpet_area has 1805 (49.1%) missing values Missing
area is highly skewed (γ1 = 29.73095613) Skewed
built_up_area is highly skewed (γ1 = 40.70657243) Skewed
carpet_area is highly skewed (γ1 = 24.33323909) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 462 (12.6%) zeros Zeros

Reproduction

Analysis started2025-03-20 22:36:57.415772
Analysis finished2025-03-20 22:37:10.122500
Duration12.71 seconds
Software versionydata-profiling vv4.15.0
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size219.9 KiB
flat
2818 
house
859 

Length

Max length5
Median length4
Mean length4.2336144
Min length4

Characters and Unicode

Total characters15567
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowhouse
3rd rowflat
4th rowhouse
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Length

2025-03-21T04:07:10.222957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:10.341082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15567
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size265.2 KiB
2025-03-21T04:07:10.803801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.869695
Min length1

Characters and Unicode

Total characters62013
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowm3m heights
2nd rowemaar mgf marbella
3rd rowramprastha primera
4th rowindependent
5th rowcentral park resorts
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.6%
heights 134
 
1.4%
Other values (783) 7497
77.5%
2025-03-21T04:07:11.353976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55465
89.4%
Space Separator 6003
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6003
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55465
89.4%
Common 6548
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Common
ValueCountFrequency (%)
6003
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

sector
Text

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
2025-03-21T04:07:11.654534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3179222
Min length3

Characters and Unicode

Total characters34262
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 65
2nd rowsector 66
3rd rowsector 37d
4th rowsector 47
5th rowsector 48
ValueCountFrequency (%)
sector 3448
46.7%
road 178
 
2.4%
sohna 166
 
2.2%
102 107
 
1.4%
85 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (107) 2921
39.6%
2025-03-21T04:07:12.204016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3803
11.1%
3706
10.8%
s 3693
10.8%
r 3693
10.8%
e 3547
10.4%
c 3499
10.2%
t 3459
10.1%
1 1070
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6212
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23302
68.0%
Decimal Number 7254
 
21.2%
Space Separator 3706
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3803
16.3%
s 3693
15.8%
r 3693
15.8%
e 3547
15.2%
c 3499
15.0%
t 3459
14.8%
a 699
 
3.0%
d 249
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1070
14.8%
0 802
11.1%
8 778
10.7%
9 763
10.5%
6 740
10.2%
7 683
9.4%
2 680
9.4%
3 664
9.2%
5 591
8.1%
4 483
6.7%
Space Separator
ValueCountFrequency (%)
3706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23302
68.0%
Common 10960
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3803
16.3%
s 3693
15.8%
r 3693
15.8%
e 3547
15.2%
c 3499
15.0%
t 3459
14.8%
a 699
 
3.0%
d 249
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Common
ValueCountFrequency (%)
3706
33.8%
1 1070
 
9.8%
0 802
 
7.3%
8 778
 
7.1%
9 763
 
7.0%
6 740
 
6.8%
7 683
 
6.2%
2 680
 
6.2%
3 664
 
6.1%
5 591
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3803
11.1%
3706
10.8%
s 3693
10.8%
r 3693
10.8%
e 3547
10.4%
c 3499
10.2%
t 3459
10.1%
1 1070
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6212
18.1%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:12.348962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2025-03-21T04:07:12.484046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:12.625285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2025-03-21T04:07:12.768575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
33333 11
 
0.3%
Other values (2641) 3509
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.3311
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:12.909285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11232.25
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1067.75

Descriptive statistics

Standard deviation23167.506
Coefficient of variation (CV)8.0210699
Kurtosis942.02903
Mean2888.3311
Median Absolute Deviation (MAD)533
Skewness29.730956
Sum10571292
Variance5.3673333 × 108
MonotonicityNot monotonic
2025-03-21T04:07:13.043717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3267
88.8%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size399.4 KiB
2025-03-21T04:07:13.452001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.236062
Min length12

Characters and Unicode

Total characters199426
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowCarpet area: 2040 (189.52 sq.m.)
2nd rowPlot area 9000(836.13 sq.m.)Carpet area: 6000 sq.ft. (557.42 sq.m.)
3rd rowSuper Built up area 1800(167.23 sq.m.)Built Up area: 1700 sq.ft. (157.94 sq.m.)Carpet area: 1600 sq.ft. (148.64 sq.m.)
4th rowPlot area 64(53.51 sq.m.)
5th rowSuper Built up area 1870(173.73 sq.m.)Built Up area: 1869 sq.ft. (173.64 sq.m.)
ValueCountFrequency (%)
area 5573
18.5%
sq.m 3655
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8700
28.9%
2025-03-21T04:07:14.006122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82758
41.5%
Decimal Number 47135
23.6%
Space Separator 26464
 
13.3%
Other Punctuation 23406
 
11.7%
Uppercase Letter 8593
 
4.3%
Close Punctuation 5535
 
2.8%
Open Punctuation 5535
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13154
15.9%
r 9456
11.4%
e 9320
11.3%
s 7567
9.1%
q 7431
9.0%
t 7324
8.8%
u 6770
8.2%
p 6767
8.2%
m 5544
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9205
19.5%
0 6628
14.1%
2 5688
12.1%
5 4714
10.0%
3 3960
8.4%
4 3711
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20389
87.1%
: 3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26464
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5535
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108075
54.2%
Latin 91351
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13154
14.4%
r 9456
10.4%
e 9320
10.2%
s 7567
8.3%
q 7431
8.1%
t 7324
8.0%
u 6770
7.4%
p 6767
7.4%
m 5544
 
6.1%
l 3701
 
4.1%
Other values (10) 14317
15.7%
Common
ValueCountFrequency (%)
26464
24.5%
. 20389
18.9%
1 9205
 
8.5%
0 6628
 
6.1%
2 5688
 
5.3%
) 5535
 
5.1%
( 5535
 
5.1%
5 4714
 
4.4%
3 3960
 
3.7%
4 3711
 
3.4%
Other values (5) 16246
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3600761
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:14.132608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976289
Coefficient of variation (CV)0.56475771
Kurtosis18.212873
Mean3.3600761
Median Absolute Deviation (MAD)1
Skewness3.4851418
Sum12355
Variance3.6009954
MonotonicityNot monotonic
2025-03-21T04:07:14.261653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.8%
7 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:14.384667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2025-03-21T04:07:14.518711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size209.4 KiB
3+
1172 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3187381
Min length1

Characters and Unicode

Total characters4849
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3+
3rd row3
4th row3
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%

Length

2025-03-21T04:07:14.650726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:14.760560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7982504
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:14.891591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0124542
Coefficient of variation (CV)0.884412
Kurtosis4.5153928
Mean6.7982504
Median Absolute Deviation (MAD)3
Skewness1.6936988
Sum24868
Variance36.149606
MonotonicityNot monotonic
2025-03-21T04:07:15.089637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size229.4 KiB
East
623 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowNorth-East
3rd rowSouth
4th rowNorth-West
5th rowSouth-East

Common Values

ValueCountFrequency (%)
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2025-03-21T04:07:15.245113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:15.548781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
east 623
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size251.6 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
438 
Old Property
303 

Length

Max length18
Median length14
Mean length13.062823
Min length9

Characters and Unicode

Total characters48032
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUndefined
2nd rowRelatively New
3rd rowRelatively New
4th rowRelatively New
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 438
 
11.9%
Old Property 303
 
8.2%
Under Construction 134
 
3.6%

Length

2025-03-21T04:07:15.680053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:15.791845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
32.4%
relatively 1646
23.8%
property 896
13.0%
old 866
 
12.5%
moderately 563
 
8.1%
undefined 438
 
6.3%
under 134
 
1.9%
construction 134
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 8563
17.8%
l 4721
 
9.8%
t 3373
 
7.0%
3239
 
6.7%
y 3105
 
6.5%
r 2623
 
5.5%
d 2439
 
5.1%
N 2239
 
4.7%
w 2239
 
4.7%
i 2218
 
4.6%
Other values (15) 13273
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37877
78.9%
Uppercase Letter 6916
 
14.4%
Space Separator 3239
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8563
22.6%
l 4721
12.5%
t 3373
 
8.9%
y 3105
 
8.2%
r 2623
 
6.9%
d 2439
 
6.4%
w 2239
 
5.9%
i 2218
 
5.9%
a 2209
 
5.8%
o 1727
 
4.6%
Other values (7) 4660
12.3%
Uppercase Letter
ValueCountFrequency (%)
N 2239
32.4%
R 1646
23.8%
P 896
13.0%
O 866
 
12.5%
U 572
 
8.3%
M 563
 
8.1%
C 134
 
1.9%
Space Separator
ValueCountFrequency (%)
3239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44793
93.3%
Common 3239
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8563
19.1%
l 4721
10.5%
t 3373
 
7.5%
y 3105
 
6.9%
r 2623
 
5.9%
d 2439
 
5.4%
N 2239
 
5.0%
w 2239
 
5.0%
i 2218
 
5.0%
a 2209
 
4.9%
Other values (14) 11064
24.7%
Common
ValueCountFrequency (%)
3239
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8563
17.8%
l 4721
 
9.8%
t 3373
 
7.0%
3239
 
6.7%
y 3105
 
6.5%
r 2623
 
5.5%
d 2439
 
5.1%
N 2239
 
4.7%
w 2239
 
4.7%
i 2218
 
4.6%
Other values (15) 13273
27.6%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:15.934888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2025-03-21T04:07:16.081019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct644
Distinct (%)38.1%
Missing1987
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.5858
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:16.220292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.88
Coefficient of variation (CV)7.5403375
Kurtosis1667.8704
Mean2379.5858
Median Absolute Deviation (MAD)650
Skewness40.706572
Sum4021500
Variance3.2194695 × 108
MonotonicityNot monotonic
2025-03-21T04:07:16.362669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
2700 33
 
0.9%
1350 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1987
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)39.2%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:16.512546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2025-03-21T04:07:16.656485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2972 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Length

2025-03-21T04:07:16.788474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:16.890112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2349 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Length

2025-03-21T04:07:16.997776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:17.101218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3339 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Length

2025-03-21T04:07:17.205732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:17.308735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3021 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Length

2025-03-21T04:07:17.410878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:17.510849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3272 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Length

2025-03-21T04:07:17.613880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:17.714724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2436 
1
1038 
2
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2436
66.2%
1 1038
28.2%
2 203
 
5.5%

Length

2025-03-21T04:07:17.825834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T04:07:17.932392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2436
66.2%
1 1038
28.2%
2 203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2436
66.2%
1 1038
28.2%
2 203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2436
66.2%
1 1038
28.2%
2 203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2436
66.2%
1 1038
28.2%
2 203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2436
66.2%
1 1038
28.2%
2 203
 
5.5%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.512918
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-03-21T04:07:18.056424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.059082
Coefficient of variation (CV)0.74195102
Kurtosis-0.88020421
Mean71.512918
Median Absolute Deviation (MAD)38
Skewness0.4590463
Sum262953
Variance2815.2662
MonotonicityNot monotonic
2025-03-21T04:07:18.200890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
42 45
 
1.2%
37 45
 
1.2%
Other values (151) 2313
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-03-21T04:07:08.268783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:58.760728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.736597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.838679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.828560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.965350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.031307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.093249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.053718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.086305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.372823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:58.862249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.832835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.937985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.924515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.096908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.169532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.187815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.155111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.191816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.472018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:58.956319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.930979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.040371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.025829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.202972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.310294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.287572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.262677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.298675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.567543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.044752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.023670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.126941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.118230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.300100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.408523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.384316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.355006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.393967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.704760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.147017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.136971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.239442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.222815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.409343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.510936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.488547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.461511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.673737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.809109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.251312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.243245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.344424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.329837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.521674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.619094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.584166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.572256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.781257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.905904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.342781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.444670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.436471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.493063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.621547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.705980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.671510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.672904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.871198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:09.005343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.439217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.538858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.532023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.605675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.712250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.797344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.763455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.760689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:07.967253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:09.108446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.539511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.640478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.631618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.723103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.816359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.897054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.855857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.884264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.062276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:09.217671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:06:59.634101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:00.737500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:01.730074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:02.838681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:03.922340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:04.995153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:05.954815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:06.973139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-21T04:07:08.159298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-21T04:07:18.320091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2310.1090.1250.0000.0000.0910.1210.2140.2250.1080.1840.1010.0470.3470.2820.1420.1150.080
area0.0001.0000.0110.6870.6240.8350.8010.0220.1160.0430.2590.0420.0370.7440.2070.0280.0150.0390.0180.948
balcony0.2310.0111.0000.2250.1760.0000.0260.0160.0790.1780.2230.0820.1970.1360.0330.2140.4410.1460.1830.306
bathroom0.1090.6870.2251.0000.8620.4650.5990.044-0.0050.1950.1790.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom0.1250.6240.1760.8621.0000.3800.5690.032-0.1040.1660.0570.0790.2910.6810.4170.5950.3170.2230.1540.800
built_up_area0.0000.8350.0000.4650.3801.0000.9691.0000.0910.0900.2890.0000.0000.6050.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.5990.5690.9691.0000.0000.1590.0000.2390.0160.0000.6130.1360.0000.0000.0000.0030.894
facing0.0910.0220.0160.0440.0321.0000.0001.0000.0000.0550.0650.0000.0290.0210.0000.0940.0360.0360.0000.000
floorNum0.1210.1160.079-0.005-0.1040.0910.1590.0001.0000.0260.2320.0330.1020.001-0.1260.4850.0840.1120.0780.152
furnishing_type0.2140.0430.1780.1950.1660.0900.0000.0550.0261.0000.2380.0640.2130.1740.0220.0850.2660.1560.1380.132
luxury_score0.2250.2590.2230.1790.0570.2890.2390.0650.2320.2381.0000.1760.1890.2150.0540.3290.3470.2280.1830.222
others0.1080.0420.0820.0700.0790.0000.0160.0000.0330.0640.1761.0000.0330.0340.0360.0260.0000.1060.0310.084
pooja room0.1840.0370.1970.2860.2910.0000.0000.0290.1020.2130.1890.0331.0000.3340.0430.2520.2520.3050.3130.157
price0.1010.7440.1360.7200.6810.6050.6130.0210.0010.1740.2150.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sqft0.0470.2070.0330.4110.4170.1320.1360.000-0.1260.0220.0540.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.3470.0280.2140.4720.5950.0000.0000.0940.4850.0850.3290.0260.2520.5430.2011.0000.0650.2410.1281.000
servant room0.2820.0150.4410.5200.3170.0000.0000.0360.0840.2660.3470.0000.2520.3690.0440.0651.0000.1610.1850.584
store room0.1420.0390.1460.2440.2230.0000.0000.0360.1120.1560.2280.1060.3050.3030.0000.2410.1611.0000.2260.046
study room0.1150.0180.1830.1760.1540.0000.0030.0000.0780.1380.1830.0310.3130.2440.0300.1280.1850.2261.0000.121
super_built_up_area0.0800.9480.3060.8190.8000.9260.8940.0000.1520.1320.2220.0840.1570.7720.2871.0000.5840.0460.1211.000

Missing values

2025-03-21T04:07:09.421090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-21T04:07:09.750393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-21T04:07:10.001264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatm3m heightssector 652.8614000.02043.0Carpet area: 2040 (189.52 sq.m.)33345.0EastUndefinedNaNNaN2040.00000001000048
1houseemaar mgf marbellasector 6619.0031666.06000.0Plot area 9000(836.13 sq.m.)Carpet area: 6000 sq.ft. (557.42 sq.m.)563+3.0North-EastRelatively NewNaNNaN6000.000000011101110
2flatramprastha primerasector 37d1.086000.01800.0Super Built up area 1800(167.23 sq.m.)Built Up area: 1700 sq.ft. (157.94 sq.m.)Carpet area: 1600 sq.ft. (148.64 sq.m.)3339.0SouthRelatively New1800.01700.01600.000000000000158
3houseindependentsector 470.9917187.0576.0Plot area 64(53.51 sq.m.)4434.0NaNRelatively NewNaN576.0NaN00000013
4flatcentral park resortssector 483.6515240.02395.0Super Built up area 1870(173.73 sq.m.)Built Up area: 1869 sq.ft. (173.64 sq.m.)333+9.0North-WestRelatively New1870.01869.0NaN000011157
5flatramprastha the atriumsector 37d0.906225.01446.0Super Built up area 1285(119.38 sq.m.)Built Up area: 1185 sq.ft. (110.09 sq.m.)Carpet area: 975 sq.ft. (90.58 sq.m.)33213.0South-EastRelatively New1285.01185.0975.00000000000237
6flatpyramid elitesector 860.457846.0574.0Carpet area: 573.54 (53.28 sq.m.)22112.0NaNUnder ConstructionNaNNaN573.50059200000030
7flathsiidc sidco shivalik apartmentsmanesar0.483840.01250.0Super Built up area 1250(116.13 sq.m.)Carpet area: 770 sq.ft. (71.54 sq.m.)2237.0SouthModerately Old1250.0NaN770.00000000000088
8flatsmart world orchardsector 611.4112250.01151.0Built Up area: 1150 (106.84 sq.m.)2222.0NaNUndefinedNaN1150.0NaN10000076
9flatdlf the skycourtsector 861.549221.01670.0Super Built up area 1929(179.21 sq.m.)Built Up area: 1780 sq.ft. (165.37 sq.m.)Carpet area: 1670 sq.ft. (155.15 sq.m.)333+15.0North-WestRelatively New1929.01780.01670.000000010000174
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793flatdlf new town heightssector 901.556556.02364.0Super Built up area 2364(219.62 sq.m.)443+1.0South-EastRelatively New2364.0NaNNaN01010081
3794flatdlf new town heightssector 901.886894.02727.0Super Built up area 2727(253.35 sq.m.)Built Up area: 2726 sq.ft. (253.25 sq.m.)Carpet area: 2725 sq.ft. (253.16 sq.m.)443+20.0SouthRelatively New2727.02726.0000002725.0010100145
3795houseiffco nagar and 17b rwasector 173.5024305.01440.0Plot area 1440(133.78 sq.m.)6533.0EastOld PropertyNaN1440.000000NaN00000044
3796houseexperion windchantssector 1127.9912519.06382.0Built Up area: 6382 (592.91 sq.m.)4632.0South-EastNew PropertyNaN6382.000000NaN011101129
3797flatsmart world orchardsector 611.5513478.01150.0Super Built up area 1150(106.84 sq.m.)2213.0NaNUnder Construction1150.0NaNNaN10000043
3798flatmaxworth city residencessector 10a0.806400.01250.0Carpet area: 1250 (116.13 sq.m.)2329.0NaNNew PropertyNaNNaN1250.000000046
3799houseindependentsector 120.656500.01000.0Carpet area: 1000 (92.9 sq.m.)6231.0NaNUndefinedNaNNaN1000.00000000
3800flatshree vardhman florasector 900.755769.01300.0Carpet area: 1300 (120.77 sq.m.)2238.0North-EastUndefinedNaNNaN1300.000000060
3801flatshree vardhman florasector 900.705177.01352.0Carpet area: 1352 (125.6 sq.m.)2327.0NorthRelatively NewNaNNaN1352.010000149
3802flathcbs sports villesohna road0.204219.0474.0Built Up area: 473.99 (44.04 sq.m.)1113.0EastNew PropertyNaN474.042156NaN00000071